Methodology of 3D Scanning of Intangible Cultural Heritage—The Example of Lazgi Dance

Traditional dance is one of the key elements of Intangible Culture Heritage (ICH). Many scientific papers concern analysis of dance sequences, classification and recognition of movements, making ICH data public, creating and visualising 3D models or software solutions for learning folklore dances. These works make it possible to preserve this disappearing art. The aim of this article is to propose a methodology for scanning folklore dances. The methodology was developed on the basis of capturing 3D data via an optical motion capture system with a full body Plug-in Gait model that allows for kinematic and kinetic analysis of motion sequences. An additional element of this research was the development of a hand model with which it is possible to precisely analyse the fingers, which play a significant role in many dances. The present methodology was verified on the basis of the Lazgi dance, included in the UNESCO ICH list. The obtained results of movement biomechanics for the dance sequence and the angles of the fingers indicate that it is universal and can be applied to dances that involve the upper and lower body parts, including hand movements.

Linear Relationships Among the Hand and Clubhead Motion Characteristics in Golf Driving in Skilled Male Golfers

The purpose of this study was to investigate the linear relationships among the hand/clubhead motion characteristics in golf driving in skilled male golfers (n = 66; handicap ≤ 3). The hand motion plane (HMP) and functional swing plane (FSP) angles, the HMP–FSP angle gaps, the planarity characteristics of the off-plane motion of the clubhead, and the attack angles were computed from the drives captured by an optical motion capture system. The HMP angles were identified as the key variables, as the HMP and FSP angles were intercorrelated, but the plane angle gaps, the planarity bias, and the attack angles showed correlations to the HMP angles primarily. Three main swing pattern clusters were identified. The parallel HMP–FSP alignment pattern with a small direction gap was associated with neutral planarity and planar swing pattern. The inward alignment pattern with a large inward direction gap was characterized by flat planes, follow-through-centric planarity, spiral swing pattern, and inward/downward impact. The outward alignment pattern with a large outward direction gap was associated with steep planes, downswing-centric planarity, reverse spiral swing, and outward/upward impact. The findings suggest that practical drills targeting the hand motion pattern can be effective in holistically reprogramming the swing pattern.

Validation of 3-Space Wireless Inertial Measurement Units Using an Industrial Robot

Inertial Measurement Units (IMUs) are beneficial for motion tracking as, in contrast to most optical motion capture systems, IMU systems do not require a dedicated lab. However, IMUs are affected by electromagnetic noise and may exhibit drift over time; it is therefore common practice to compare their performance to another system of high accuracy before use. The 3-Space IMUs have only been validated in two previous studies with limited testing protocols. This study utilized an IRB 2600 industrial robot to evaluate the performance of the IMUs for the three sensor fusion methods provided in the 3-Space software. Testing consisted of programmed motion sequences including 360° rotations and linear translations of 800 mm in opposite directions for each axis at three different velocities, as well as static trials. The magnetometer was disabled to assess the accuracy of the IMUs in an environment containing electromagnetic noise. The Root-Mean-Square Error (RMSE) of the sensor orientation ranged between 0.2° and 12.5° across trials; average drift was 0.4°. The performance of the three filters was determined to be comparable. This study demonstrates that the 3-Space sensors may be utilized in an environment containing metal or electromagnetic noise with a RMSE below 10° in most cases.

Foot Position Measurement during Assistive Motion for Sit-to-Stand Using a Single Inertial Sensor and Shoe-Type Force Sensors

Assistive motion for sit-to-stand causes lower back pain (LBP) among caregivers. Considering previous studies that showed that foot position adjustment could reduce lumbar load during assistive motion for sit-to-stand, quantitative monitoring of and instructions on foot position could contribute toward reducing LBP among caregivers. The present study proposes and evaluates a new method for the quantitative measurement of foot position during assistive motion for sit-to-stand using a few wearable sensors that are not limited to the measurement area. The proposed method measures quantitative foot position (anteroposterior and mediolateral distance between both feet) through a machine learning technique using features obtained from only a single inertial sensor on the trunk and shoe-type force sensors. During the experiment, the accuracy of the proposed method was investigated by comparing the obtained values with those from an optical motion capture system. The results showed that the proposed method produced only minor errors (less than 6.5% of body height) when measuring foot position during assistive motion for sit-to-stand. Furthermore, Bland–Altman plots suggested no fixed errors between the proposed method and the optical motion capture system. These results suggest that the proposed method could be utilized for measuring foot position during assistive motion for sit-to-stand.

Gap Reconstruction in Optical Motion Capture Sequences Using Neural Networks

Optical motion capture is a mature contemporary technique for the acquisition of motion data; alas, it is non-error-free. Due to technical limitations and occlusions of markers, gaps might occur in such recordings. The article reviews various neural network architectures applied to the gap-filling problem in motion capture sequences within the FBM framework providing a representation of body kinematic structure. The results are compared with interpolation and matrix completion methods. We found out that, for longer sequences, simple linear feedforward neural networks can outperform the other, sophisticated architectures, but these outcomes might be affected by the small amount of data availabe for training. We were also able to identify that the acceleration and monotonicity of input sequence are the parameters that have a notable impact on the obtained results.

Validation of a Wireless Sensor System for the Estimation of Cumulative Lumbar Loads in Occupational Settings

This study aimed to evaluate the accuracy of 3D L5/S1 moment estimates from a wearable inertial motioncapture system during manual lifting tasks. Reference L5/S1 moments were calculated using inversedynamics bottom-up and top-down laboratory models, based on the data from a measurement systemcomprising optical motion capture and force plates. Nine groups of four subjects performed tasks consistingof lifting and lowering 10 lbs. load with three different heights and asymmetry angles. As a measure ofsystem performance, the root means square errors and absolute peak errors between models werecompared. Also, repeated measures analyses of variance were calculated comparing the means and theabsolute peaks of the estimated moments. The results suggest that most of the estimates obtained from thewireless sensor system are in close correspondence when comparing the means, and more variability isobserved when comparing peak values to other models calculating estimates of L5/S1 moments.

Proposed Mobility Assessments with Simultaneous Full-Body Inertial Measurement Units and Optical Motion Capture in Healthy Adults and Neurological Patients for Future Validation Studies: Study Protocol

Healthy adults and neurological patients show unique mobility patterns over the course of their lifespan and disease. Quantifying these mobility patterns could support diagnosing, tracking disease progression and measuring response to treatment. This quantification can be done with wearable technology, such as inertial measurement units (IMUs). Before IMUs can be used to quantify mobility, algorithms need to be developed and validated with age and disease-specific datasets. This study proposes a protocol for a dataset that can be used to develop and validate IMU-based mobility algorithms for healthy adults (18–60 years), healthy older adults (>60 years), and patients with Parkinson’s disease, multiple sclerosis, a symptomatic stroke and chronic low back pain. All participants will be measured simultaneously with IMUs and a 3D optical motion capture system while performing standardized mobility tasks and non-standardized activities of daily living. Specific clinical scales and questionnaires will be collected. This study aims at building the largest dataset for the development and validation of IMU-based mobility algorithms for healthy adults and neurological patients. It is anticipated to provide this dataset for further research use and collaboration, with the ultimate goal to bring IMU-based mobility algorithms as quickly as possible into clinical trials and clinical routine.

Assessing the Bowing Technique in Violin Beginners Using MIMU and Optical Proximity Sensors: A Feasibility Study

Bowing is the fundamental motor action responsible for sound production in violin playing. A lot of effort is required to control such a complex technique, especially at the beginning of violin training, also due to a lack of quantitative assessments of bowing movements. Here, we present magneto-inertial measurement units (MIMUs) and an optical sensor interface for the real-time monitoring of the fundamental parameters of bowing. Two MIMUs and a sound recorder were used to estimate the bow orientation and acquire sounds. An optical motion capture system was used as the gold standard for comparison. Four optical sensors positioned on the bow stick measured the stick–hair distance. During a pilot test, a musician was asked to perform strokes using different sections of the bow at different paces. Distance data were used to train two classifiers, a linear discriminant (LD) classifier and a decision tree (DT) classifier, to estimate the bow section used. The DT classifier reached the best classification accuracy (94.2%). Larger data analysis on nine violin beginners showed that the orientation error was less than 2°; the bow tilt correlated with the audio information (r134=−0.973, 95% CI −0.981,−0.962, p<0.001). The results confirmed that the interface provides reliable information on the bowing technique that might improve the learning performance of violin beginners.

Experimental Study of Military Crawl as a Special Type of Human Quadripedal Automatic Locomotion

The biomechanics of military crawl locomotion is poorly covered in scientific literature so far. Crawl locomotion may be used as a testing procedure which allows for the detection of not only obvious, but also hidden locomotor dysfunctions. The aim of the study was to investigate the biomechanics of crawling among healthy adult participants. Eight healthy adults aged 15–31 (four women and four men) were examined by means of a 3D kinematic analysis with Optitrack optical motion-capture system which consists of 12 Flex 13 cameras. The movements of the shoulder, elbow, knee, and hip joints were recorded. A person was asked to crawl 4 m on his/her belly. The obtained results including space-time data let us characterize military crawling in terms of pelvic and lower limb motions as a movement similar to walking but at a more primitive level. Progressive and propulsive motions are characterized as normal; additional right–left side motions—with high degree of reciprocity. It was found that variability of the left-side motions is significantly lower than that of the right side (Z = 4.49, p < 0.0001). The given normative data may be used as a standard to estimate the test results for patients with various pathologies of motor control (ataxia, abasia, etc.).